We are happy to announce the 0.2.0 (beta) release of scikits.statsmodels.
This is both a bug-fix and new feature release.
Download
--------------
You can easy_install (or PyPI URL:
<http://pypi.python.org/pypi/scikits.statsmodels/>)
Source downloads: <http://sourceforge.net/projects/statsmodels/>
Development branches: <http://code.launchpad.net/statsmodels>
Note that the trunk branch on launchpad is almost always stable and
has the most up to date changes since our releases are so few and far
between.
Documentation
----------------------
<http://statsmodels.sourceforge.net/>
We invite you to install, kick the tires, and make bug reports
and feature requests.
Feedback can either be on scipy-user or the mailing list at
<http://groups.google.com/group/pystatsmodels?hl=en>
Bug tracker: <https://bugs.launchpad.net/statsmodels>
Main Changes in 0.2.0
---------------------------------
* Improved documentation and expanded and more examples
* Added four discrete choice models: Poisson, Probit, Logit, and
Multinomial Logit.
* Added PyDTA. Tools for reading Stata binary datasets (*.dta) and putting
them into numpy arrays.
* Added four new datasets for examples and tests.
* Results classes have been refactored to use lazy evaluation.
* Improved support for maximum likelihood estimation.
* bugfixes
* renames for more consistency
RLM.fitted_values -> RLM.fittedvalues
GLMResults.resid_dev -> GLMResults.resid_deviance
Sandbox
-------------
We are continuing to work on support for systems of equations models, panel data
models, time series analysis, and information and entropy econometrics in the
sandbox. This code is often merged into trunk as it becomes more robust.
Cheers,
Josef and Skipper